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This post has been authored by Dina Kretzschmann and Alessandro Berti.

Inventory management is crucial for a retails company success, as it directly impacts sales and costs. The core processes affecting inventory management are Order-to-Cash (O2C) and Purchase-to-Pay (P2P) processes. Efficiently managing these processes ensures product availability aligns with customer demand, to avoid understock (leading to lost sales) and overstock (incurring unnecessary costs) situations [1].

Current work on inventory management optimization includes (1) exact mathematical optimization models [2], (2) business management techniques [3], (3) ETL methodologies [4], and (4) traditional/object-centric process mining approaches [5]. However, gaps remain, such as the lack of standardized formalization, static assessments of key performance indicator without root cause analysis, missing links between optimization models and event data, and non-generalizable results [6].

We address these gaps by introducing a generalized object-centric data model (OCDM) for inventory management. This OCDM is enriched with relevant metrics, including Economic Order Quantity (EOQ), Reorder Point (ROP), Safety Stock (SS), Maximum Stock Level (Max), and Overstock (OS), enabling a comprehensive event-data-driven process behavior assessments and the definition of optimization measures (see Figure 1).

Figure 1 Outline of the contributions

We applied our approach to real-life O2C and P2P processes of a pet retailer utilizing the Logomate system for demand forecasting and replenishment, and SAP system for procurement and sales. The pet retailer faces issues in O2C and P2P processes leading to understock and overstock situations worth several million euros. In particular, through the standardized assessment of the interactions between different business objects we identified process behavior leading to understock and overstock situations. We quantified the frequency of these behaviors and conducted a root cause analysis, enabling the definition of optimization measures for the demand forecasting model and adjustments in the supplier contracts. The pet retailer acknowledged the added value of the results. Our approach is reproducible and generalizable with any object-centric event log following the proposed OCDM.

[1] Arnold, D., Isermann, H., Kuhn, A., Tempelmeier, H., Furmans, K.: Handbuch Logistik. Springer (2008)

[2] Tempelmeier, H.: Bestandsmanagement in supply chains. BoD–Books on Demand (2005)

[3] Rahansyah, V.Z., Kusrini, E.: How to Reduce Overstock Inventory: A Case Study. International Journal of Innovative Science and Research Techno (2023)

[4] Dong, R., Su, F., Yang, S., Xu, L., Cheng, X., Chen, W.: Design and application on metadata management for information supply chain. In: ISCIT 2021. pp. 393–396. IEEE (2016)

[5] Kretzschmann, D., Park, G., Berti, A., van der Aalst, W.M.: Overstock Problems in a Purchase-to-Pay Process: An Object-Centric Process Mining Case Study. In: CAiSE 2024 Workshops. pp. 347–359. Springer (2024)

[6] Asdecker, B., Tscherner, M., Kurringer, N., Felch, V.: A Dirty Little Secret? Conducting a Systematic Literature Review Regarding Overstocks. In: Logistics Management Conference. pp. 229–247. Springer (2023)

This post has been authored by Nina Graves.

In today’s business landscape, companies are faced with the urgent need to make their processes more sustainable. Process mining techniques, known for their capability to provide valuable insights and support process improvement, are gaining increasing attention to support the transformation towards more sustainable processes [1]. To this end, we explore how process mining techniques can be enhanced to better support the transformation to more sustainable business processes. Initially, we identified the types of business processes that are most relevant for sustainability transformation: particularly production and logistics processes [2]. However, these processes are often challenging to analyse because (object-centric) process mining techniques make certain assumptions that do not always hold true:

1. Every relevant process instance can be “tracked” in the event log using a unique identifier.
2. The execution of an event depends on time or the “state” of the involved objects (previously executed activities, object attributes, object relationships).
3. Two process executions are independent of each other.

Figure 1 – Decoupled Example Process (SP: Sub-Process)

Now imagine you are a company selling pencil cases (Figure 1):

You buy cases and pens from your suppliers (SP 1), adjust the cases and add the pens to create the final product (SP 2). Finally, you fulfil the incoming customer orders by sending the number of pencil cases you demand (SP 3). Additionally, you must ensure you always have enough pens, cases, and pencil cases to cover the incoming customer demand without keeping inventory levels too high. You would like to support your processes using PM techniques both to support your process and to analyse your scope3 emissions for the pencil cases you sell to end customers. You now face three problems: 1) You cannot detect the full end-to-end process, as there is no unique identifier for either the products you buy or the ones you sell. 2) The quantity of products you are considering varies between the sub-processes and even within the individual process executions (e.g. the demand in two customer orders is not necessarily the same). 3) You cannot consider the overall inventory management process, as it depends on the available quantities of products you cannot explicitly capture in the event log.

To bridge this gap, we are currently working on the extension of process mining techniques to support the perks of production and logistics processes. To do so, we are developing process mining techniques for the joint consideration of decoupled sub-processes [2]. Combining them with decoupling points (triangles in Figure 1), we allow for the joint consideration of (sub-)processes not combined by identifiable objects as well as another way of describing the state a process is in. This quantity state describes the count of items associated with one of the decoupling points and can be changed by executing specific events (Figure 2), e.g., “add to inventory” increases the number of products available in the incoming goods inventory.

Figure 2 – Development of the Stock Levels over Time (Quantity State)

The extension to process mining techniques we are working on allows for a more explicit consideration of quantities and their impact on the execution of events, e.g., the execution of “place replenishment order” depends on the number of pencils and cases available in the incoming goods inventory and the number of finished pencil cases. We are excited to dive deeper into this area of quantity-related process mining, as it offers many new possibilities for combining “disconnected” sub-processes and detecting quantity dependencies across multiple process executions.

References

[1] Horsthofer-Rauch, J., Guesken, S. R., Weich, J., Rauch, A., Bittner, M., Schulz, J., & Zaeh, M. F. (2024). Sustainability-integrated value stream mapping with process mining. Production & Manufacturing Research, 12(1), 2334294. https://doi.org/10.1080/21693277.2024.2334294

[2] Graves, N., Koren, I., & van der Aalst, W. M. P. (2023). ReThink Your Processes! A Review of Process Mining for Sustainability. 2023 International Conference on ICT for Sustainability (ICT4S), 164–175. https://doi.org/10.1109/ICT4S58814.2023.00025

[3] Graves, N., Koren, I., Rafiei, M., & van der Aalst, W. M. P. (2024). From Identities to Quantities: Introducing Items and Decoupling Points to Object-Centric Process Mining. In J. De Smedt & P. Soffer (Eds.), Process Mining Workshops (Vol. 503, pp. 462–474). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56107-8_35